1. Field of the Invention
This invention relates generally to communication systems, and, more particularly, to wireless communication systems.
2. Description of the Related Art
Conventional wireless communication systems include one or more base stations, which may also be referred to as node-Bs or Access Network (AN), for providing wireless connectivity to one or more mobile units, which may also be referred to using terms such as user equipment, subscriber equipment, and Access Terminals (AT). Exemplary mobile units include cellular telephones, personal data assistants, smart phones, text messaging devices, laptop computers, desktop computers, and the like. A base station may provide wireless connectivity concurrently to one or more mobile units, such as the mobile units in a geographical area, or cell, associated with the base station. Each base station has a limited budget of radio resources that may be used to provide a wireless connectivity. Exemplary radio resources include total transmission power, available channel codes, time slots, modulation/coding sets, and the like.
Base stations typically include a radio resource management mechanism, such as a scheduler, which is used to allocate radio resources to the mobile units. Conventional radio resource management mechanisms may implement either efficiency-driven algorithms (e.g., a maximum rate scheduler) or fairness-driven algorithms (e.g., a round-robin scheduler). The efficiency-driven algorithms attempt to achieve the highest efficiency of radio resource usage, e.g., by allocating more radio resources to the mobile units that are able to support transmissions at the highest data rates. However, efficiency driven algorithms typically sacrifice fairness because mobile units that are unable to support high data transfer rates may not be allocated radio resources. Fairness-driven algorithms attempt to allocate the radio resources fairly, e.g., by allocating resources sequentially to each mobile unit having a wireless connection with the base station. However, fairness-driven algorithms methods may sacrifice efficiency because the base station may allocate radio resources to mobile units that have comparatively poor quality connections to the base station.
Conventional schedulers use mathematical models to allocate resources to mobile units. However, each base station is typically required to provide service to many mobile units and each mobile unit may have different (and potentially time variable) intra-user quality of service (QoS) requirements and inter-user quality of service requirements. These requirements may be difficult or impossible to model using mathematical formulae. Moreover, different application scenarios that may be implemented on the base station and/or the mobile unit may have different resource allocation requirements. Accommodating these application-dependent requirements may be difficult or impossible using current methods that implement a single mathematical model in the scheduling algorithm. What is more, resource scheduling policies or schemes from operators may dynamically adjust due to fast changing market motivations. Consequently, radio resource allocation algorithms based on mathematical models may sacrifice accuracy and/or flexibility, which may degrade the effectiveness and functionality of the radio resource allocation algorithms when applied to complex systems that may have time-variant requirements.